ABSTRACT Falls are a major cause of morbidity and mortality in dementia. The first step in any fall prevention program is to identify patients at risk. Unfortunately, existing methods for fall risk screening are not adequate for patients with dementia. In a recent pilot study, we demonstrated that conventional fall risk assessment methods are not able to predict prospective falls in people with dementia. However, in the same study, we demonstrated that physical activity (PA) parameters are independent predictors of fall risk in persons with dementia. An advantage of assessing fall risk using PA-based parameters is that this can be done remotely and continuously, which is critical for early intervention. Through our prior STTR awards, we have developed and commercialized a wearable platform to automatically detect falls and remotely monitor day-to-day fluctuations in risk of falling in older adults without dementia. In our currently funded NIH CRP project we aim to further improve this system by, among other things, adapting it for a wrist-worn form factor. With this supplementary funding, we plan to expand our efforts under the CRP project to include the development of a PA-based fall risk assessment method specifically for patients with dementia. This is significant because PA-based fall risk algorithms for patients without dementia should not be used for patients with dementia because their daily activities are different. Furthermore, we have shown that the PA- parameters that are predictive of falls in community dwelling older adults are not the same as the PA-parameters that are predictive of falls in dementia. Therefore, it is necessary to develop a unique fall risk assessment algorithm for the dementia population. In the project supplement, we propose to expand the clinical study to include patient with dementia. We will recruit a cohort of 70 people with confirmed dementia (age 75+) and enroll them in a shortened clinical study of 6 months duration (compared to 1 year for the CRP project). Based on these data, and records of confirmed falls during the study period, we will develop a PA-based fall risk assessment algorithm specific for patients with dementia. We will also collect conventional assessments including performance-based tests (Timed-up-and-go, Performance-Oriented-Mobility-Assessment, 5-chair-stand), and questionnaires (cognition, ADL-status, fear of falling, depression, history of falls) for comparison. Finally, as a part of this project supplement we will integrate the newly developed fall risk algorithm into commercially available medical alert devices, based on our currently established partnerships.